code: def get_lexicons(self, num_terms=10):
      x_max = self.axes["x"].max()
      x_med = self.axes["x"].median()
      x_min = self.axes["x"].min()
      y_max = self.axes["y"].max()
      y_med = self.axes["y"].median()
      y_min = self.axes["y"].min()
  
      lexicons = {}
      for label, [x_coord, y_coord] in [
          ["a", [x_min, y_max]],
          ["not_a", [x_max, y_min]],
          ["b", [x_max, y_max]],
          ["not_b", [x_min, y_min]],
          ["a_and_b", [x_med, y_max]],
          ["not_a_and_not_b", [x_med, y_min]],
          ["b_and_not_a", [x_max, y_med]],
          ["a_and_not_b", [x_min, y_med]],
      ]:
          # scores = np.linalg.norm(np.array([self.axes['x'] - x_coord, self.axes['y'] - y_coord]), 2, axis=0)
          scores = self._distance_measure.distances(
              x_coord, y_coord, self.axes["x"], self.axes["y"]
          )
  
          lexicons[label] = list(self.axes.index[np.argsort(scores)])[
              :num_terms
          ]
      return lexicons
  
file_path: scattertext/scattertext/semioticsquare/SemioticSquareFromAxes.py
imports: import numpy
  import pandas
  from scattertext.distancemeasures.EuclideanDistance import EuclideanDistance
  from scattertext.semioticsquare.SemioticSquare import SemioticSquareBase
label_desc: Use image node_id 4 for calling the SemioticSquareFromAxes obj's underlying member method code with example usage: obj.get_lexicons(num_terms) and returns: lexicons